Biostatistics Core

The planned Biostatistics Core provides (bio)statistical support that enables Center investigators to incorporate rigorous statistical analyses and advanced statistical methods into their research. This Core will be a powerful unifying force in the UW Center for Clean Air Research by engaging in interdisciplinary interactions, supporting multiple projects, and ensuring careful attention to proper application of statistical methods. It will provide the crucial link between content area and statistical methodology experts that fosters the most scientifically relevant methodological development.

The Biostatistics Core activities include statistical consultation, advice on database management and compilation, statistical analysis, statistical methods development, and dissemination. This
Core will follow an active consultation model and it will take a proactive approach to statistical collaboration in Center research. It supports all Center projects to varying degrees depending upon anticipated need.

Objectives

The overall objective of this Core is to support the database management and statistical needs of all Center activities. This will be achieved through the following specific objectives:

Advise Center projects on data management and compilation

Ensure quality statistical design and analysis of Center research

Implement novel statistical methods that are required for Center projects: Develop an analytical framework for quantifying the health effects of different mixtures of air pollution components in a cohort study (Project 1 and Project 5)

Identify additional statistical methodological research that will advance Center projects

Communicate and disseminate Center findings

Approach

The Biostatistics Core will serve every Center project through its services and activities. These are aligned with the objectives.

Expected Results

There are two primary outcomes provided by this Core: 1) to enhance the statistical rigor of all research conducted in all five projects of this proposal and thus improve their impact on scientific understanding and clean air policy, and 2) to advance understanding of methods for making inference about multi-pollutant mixtures in epidemiologic studies; this will result in reduced uncertainty in health risk assessments, and better, more targeted clean air policy.

The outputs will be papers in the peer-reviewed literature, both in the content-specific literature (e.g. exposure, toxicology, epidemiology) and in the statistics literature, as well as new statistical methodology with accompanying computer code to implement this methodology, and predictions of pollution exposure for MESA Air subjects that can be incorporated into this and other epidemiologic cohort studies.